Control method and system for optimizing coiling side guide backpressure loop of hot-rolled plate factory

Abstract

The invention discloses a control method for optimizing a coiling side guide backpressure loop of a hot-rolled plate factory, relates to the technical field of strip steel control, and solves the problems that in the strip steel production process, when the conditions of strip steel width change, head and tail tension loss, rolling force fluctuation and the like occur, the condition that clamping is not tight or too tight possibly occurs due to a fixed opening and constant backpressure, and the production efficiency is poor. The production quality of the strip steel is influenced. Real-time data of a side plate and a production line are collected through a data sensor; dynamically adjusting the pressure of the side plate according to the real-time data; testing the adjusted pressure, and constraining the pressure according to a test result; predicting the pressure according to the position data of the strip steel; adjusting the opening degree of the valve port based on the prediction result; and the production efficiency and the production quality of the strip steel are improved.

Description

Technical Field

[0001] The present invention belongs to the field of strip steel control, and relates to a control technology for optimizing a back pressure circuit, in particular to a control method for optimizing a coiling side guide back pressure circuit in a hot rolling mill. Background Art

[0002] The optimization of the side guide back pressure circuit of the coiling machine in the hot rolling mill is a technical improvement measure for the hydraulic system of the side guide device of the coiler in hot rolling production. Its core is to improve the stability, accuracy and reliability of the side guide device by adjusting the back pressure parameters, optimizing the hydraulic circuit design or introducing intelligent control strategies; in the hot rolling coiling process, the side guide device drives the guide plate through the hydraulic cylinder to perform transverse positioning of the strip to ensure that the edge of the strip is aligned with the coiler reel to prevent defects such as deviation and towering. Back pressure is a key parameter in the hydraulic system to maintain the stability of the mechanism. In the side guide device, the back pressure circuit balances the pressure difference between the two chambers of the hydraulic cylinder by adjusting the return oil pressure to prevent the guide plate from shaking or overshooting due to inertia or external force impact, thereby ensuring positioning accuracy.

[0003] The existing side guide back pressure circuit optimization method determines the opening and back pressure of the side guide plate according to factors such as the material and width of the strip, and processes the strip; however, during the strip production process, when the strip width changes, the head and tail are out of tension, the rolling force fluctuates, etc., the fixed opening and constant back pressure may result in insufficient clamping or excessive clamping, affecting the production quality of the strip. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a control method for optimizing the guide back pressure circuit on the coiling side of a hot-rolled plate mill, which is used to solve the technical problem that in the strip production process, when the strip width changes, the head and tail tension is lost, the rolling force fluctuates, etc., the fixed opening and constant back pressure may lead to the situation that the clamping is not tight or the clamping is too tight, affecting the strip production quality.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides a control method for optimizing a coiling-side guide back pressure circuit in a hot-rolled plate mill, comprising: Collect real-time data of side panels and production lines through data sensors; Dynamically adjust the pressure of the side panels based on real-time data; Test the adjusted pressure; detect and adjust abnormalities based on the test results; Predict the pressure based on the position data of the strip; The valve opening is adjusted based on the prediction results.

[0006] Based on this, by collecting side plate and production line data in real time and dynamically adjusting the side plate pressure accordingly, it is possible to ensure that the side plate pressure is always in the most suitable state for production. This helps to reduce surface defects of the strip caused by inappropriate pressure, such as indentations and ripples, and improve the surface quality and overall quality stability of the strip. Testing the adjusted pressure and detecting and adjusting abnormalities based on the results can quickly detect and correct pressure abnormalities during the production process, avoiding the production of a large number of substandard products due to the continuous effect of abnormal pressure, reducing the scrap rate, and improving the product qualification rate. Real-time data collection and dynamic pressure adjustment mechanisms enable the production line to quickly respond to various changes in the production process, without the need for frequent manual intervention and long debugging, reducing production interruptions and waiting time. Through timely detection and adjustment of pressure abnormalities, it can effectively prevent equipment failures and production accidents caused by pressure problems, reduce the frequency of equipment downtime and maintenance, ensure the continuous and stable operation of the production line, and thus improve overall production efficiency.

[0007] Preferably, the dynamically adjusting the pressure of the side panels according to real-time data includes: Retrieve real-time data, including: side guide cylinder back pressure value, side guide opening, production line speed, strip width, and strip position; The side plate back pressure value is analyzed according to the real-time data to obtain the theoretical back pressure value; the side guide plate is adjusted according to the compression of the side guide plate cylinder in the real-time data and the theoretical back pressure value.

[0008] Preferably, the analyzing the side plate back pressure value according to the real-time data includes: Compare the side guide opening with the strip width and calculate the deviation between the two; integrate the production line speed within a set time period and mark the segments according to the acceleration of the production line; Construct a backpressure value analysis function: ; Wherein, k represents the process coefficient; is the material strength of the strip; H is the thickness of the strip; W is the width of the strip; V represents the production line speed; It is the maximum speed of the production line within the set time period; L represents the opening of the side guide plate; is the distance between the strip position and the starting position; The back pressure value of the side plate is calculated according to the back pressure value analysis function to obtain the theoretical back pressure value.

[0009] Preferably, the adjusting of the side guide plate according to the side guide plate cylinder being pressed and the theoretical back pressure value in the real-time data includes: The back pressure value of the side guide plate cylinder is processed by using a sliding mean filter; the difference between the back pressure value of the side guide plate cylinder and the theoretical back pressure value is calculated to obtain the pressure deviation; By formula Calculate the deviation change rate of the back pressure value; where e(k) is the pressure deviation at the current moment; e(k-1) is the pressure deviation at the previous moment; and T is the sampling period. The absolute values ​​of the pressure deviation and the deviation change rate are compared with the pressure database to obtain corresponding adjustment parameters; the side guide plate is adjusted according to the adjustment parameters.

[0010] Preferably, the testing of the adjusted pressure includes: Monitor the adjusted production line and extract characteristic data from the production line within a set time period; the characteristic data includes: real-time pressure data, response delay time, and strip position; By formula Calculates the pressure fluctuation rate within a set time period ;in, It is the maximum value of real-time pressure data within the set time period; It is the minimum value of pressure data within the set time period; is the theoretical back pressure value; By formula Calculate the position-pressure coupling coefficient; where, The change in real-time pressure within the set time period; The change in the strip position within the set time; The pressure fluctuation rate, response delay time and position-pressure coupling coefficient analysis are compared with the corresponding characteristic thresholds; when the pressure fluctuation rate, response delay time and position-pressure coupling coefficient are all less than the corresponding characteristic thresholds, the test result is marked as passed; otherwise, the test result is marked as failed.

[0011] Preferably, the detecting and adjusting of anomalies according to the test results includes: If the test result is passed, maintain pressure control; otherwise, analyze the reasons for the different tests; if the pressure fluctuation rate is greater than the corresponding characteristic threshold, re-analyze the theoretical back pressure value and strengthen pressure control; When the response delay time is greater than the corresponding characteristic threshold, the communication connection is checked and reconnected; when the position-pressure coupling coefficient is greater than the corresponding characteristic threshold, the position of the strip is re-detected and marked.

[0012] Preferably, the predicting of the pressure according to the position data of the strip steel includes: Retrieve the historical position data and corresponding pressure data of the strip steel; integrate the historical position data and pressure data into a historical analysis sequence; The real-time position of the strip and the historical analysis sequence are integrated into a pressure prediction sequence; the pressure prediction model is called, and the pressure prediction sequence is input into the pressure prediction model to obtain the corresponding predicted pressure; wherein, the pressure prediction model is based on artificial intelligence model training.

[0013] Preferably, the pressure prediction model is trained based on an artificial intelligence model and includes: Select appropriate models and deep learning frameworks from the artificial intelligence model library; construct the model based on the deep learning framework to obtain a constructed model; Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the pressure prediction sequence and standard output data consistent with the content attributes of the predicted pressure; Divide the standard data set into training set, validation set and test set according to the set ratio; use the training set to train the constructed model; use the validation set to adjust the internal parameters of the constructed model; use the test set to test the constructed model and obtain the test indicators; When the test index is greater than the index threshold, the trained construction model is marked as a stress prediction model; otherwise, the stress test model is rebuilt and trained.

[0014] Preferably, the adjusting the valve opening based on the prediction result includes: Fitting historical pressure data of the strip into a pressure change curve; calculating a prediction threshold value based on the pressure change curve and a set maximum change rate; and comparing the predicted pressure with the prediction threshold value; When the predicted pressure is less than the predicted threshold, the valve opening is not adjusted; otherwise, the absolute value of the difference between the two is calculated to obtain the pressure difference; the pressure difference is matched with the corresponding mapping relationship table to obtain the adjustment range, and the valve opening is adjusted according to the adjustment range.

[0015] A second aspect of the present invention provides a control method system for optimizing a coiling-side guide back pressure circuit in a hot-rolled plate mill, comprising: a collection unit and a processing unit; The acquisition unit is used to collect real-time data of the side panels and production line through data sensors; The processing unit is used to dynamically adjust the pressure of the side plate according to real-time data; test the adjusted pressure and constrain the pressure according to the test results; predict the pressure according to the position data of the strip; and adjust the valve opening based on the prediction results.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The back pressure value analysis function constructed by the present invention comprehensively considers multiple key factors such as the material strength, thickness, width, production line speed, side guide plate opening, and the distance between the strip position and the starting position, providing a precise target value for subsequent pressure adjustment, helping to ensure that the pressure applied by the side guide plate to the strip is just right, reducing quality problems such as strip deformation and surface scratches caused by inappropriate pressure, and improving product qualification rate; the side guide plate cylinder back pressure value is processed by sliding mean filtering, which can smooth the data, remove noise interference, and make the obtained pressure value more accurate and reliable. On this basis, the pressure deviation and deviation change rate are calculated, and compared with the pressure database to obtain the adjustment parameters, and then the side guide plate is adjusted to achieve dynamic and precise control of the pressure, further ensuring product quality; the adjusted production line is monitored, the characteristic data within the set time period is extracted, and the pressure fluctuation rate is calculated. This can understand the stability of the production line pressure in real time, detect abnormal pressure fluctuations in time, avoid the impact of unstable pressure on production, and ensure the continuity and stability of the production process.

[0017] 2. The present invention retrieves the historical position data and corresponding pressure data of the steel strip and integrates them into a historical analysis sequence. The accumulated data resources provide rich and representative data samples for subsequent pressure prediction. The analysis of these historical data can help us understand the pressure change pattern of the steel strip at different positions, thereby laying a solid foundation for accurate pressure prediction; the historical pressure data of the steel strip is fitted into a pressure change curve, and the prediction threshold is calculated based on the curve and the set maximum change rate, which helps to avoid unnecessary adjustments due to small fluctuations in pressure and ensure the stability of the production process; decisions are made based on the comparison results of the predicted pressure and the predicted threshold; it is ensured that the pressure on the steel strip is always within a reasonable range, thereby improving product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of the steps of the overall scheme of the present invention; Figure 2 A schematic diagram of the real-time data analysis and adjustment steps of the present invention; Figure 3 Schematic diagram of the pressure prediction and opening adjustment steps of the present invention; Figure 4 Schematic diagram of the working steps of the device of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 The first embodiment of the present invention provides a control method for optimizing a coiling-side guide back pressure circuit in a hot-rolled plate mill, comprising: S101, collecting real-time data of the side panels and production line through data sensors; S102, dynamically adjusting the pressure of the side panels according to real-time data; S103, testing the adjusted pressure; detecting and adjusting abnormalities based on the test results; S104, predicting the pressure according to the position data of the strip; S105: Adjust the valve opening based on the prediction result.

[0022] In a possible implementation of the embodiment of the present invention, combined with Figure 1 , Figure 2 Steps S102-S103 can be implemented by the following S201-S207, which are described in detail below: S201. Retrieve real-time data; compare the side guide plate opening with the strip width and calculate the deviation between the two; integrate the production line speed within a set time period and mark the segments according to the acceleration of the production line.

[0023] The real-time data includes: side guide cylinder back pressure value, side guide opening, production line speed, strip width and strip position.

[0024] For example, assume that the sampling period T is 1 second and the time period is set to 10 seconds. The real-time data collected is as follows: strip thickness H = 20 mm (constant); strip width W = 1000 mm (constant); strip material strength σ = 400 MPa (assuming ordinary steel); process coefficient k = 0.85; speed influence coefficient α = 0.12; strip position: starting from the starting point S0 = 10 m (to avoid calculation problems caused by L0 = 0) and increasing over time. Based on actual experience, the thresholds are set as follows: pressure fluctuation rate threshold: 5%; response delay time threshold: 2s; position pressure coupling coefficient threshold: 0.1MPa / m; The collected real-time data is shown in the following table:

[0025] Table 1: Schematic table of real-time data Velocity integration and acceleration segment marking: a(1)= 0.5; a(2)=0.5, a(3)=0.5; a(4)=0.5; a(5)=0.5 → acceleration segment (marked as segment A).

[0026] a(6)=0.5→Continue to accelerate (segment A).

[0027] a(7)=0→uniform speed segment (marked as segment B).

[0028] a(8)=-0.5; a(9)=-0.5; a(10)=-0.5→Deceleration section (marked as section C).

[0029] S202, constructing a back pressure value analysis function; calculating the side plate back pressure value according to the back pressure value analysis function to obtain a theoretical back pressure value.

[0030] Among them, the back pressure value analysis function: ; k represents the process coefficient; is the material strength of the strip; H is the thickness of the strip; W is the width of the strip; V represents the production line speed; It is the maximum speed of the production line within the set time period; L represents the opening of the side guide plate; is the distance between the strip position and the starting position.

[0031] For example, the theoretical back pressure values ​​from t=0 to t=10 are calculated based on the collected real-time data: 2.15; 2.18; 2.20; 2.22; 2.24; 2.25; 2.26; 2.26; 2.25; 2.24; 2.22.

[0032] S203, using the sliding mean filter method to process the side guide plate cylinder back pressure value; calculating the difference between the side guide plate cylinder back pressure value and the theoretical back pressure value to obtain the pressure deviation; using the formula Calculate the deviation change rate of the back pressure value; compare the absolute value of the pressure deviation and the deviation change rate with the pressure database to obtain corresponding adjustment parameters; and adjust the side guide plate according to the adjustment parameters.

[0033] Wherein, e(k) is the pressure deviation at the current moment; e(k-1) is the pressure deviation at the previous moment; and T is the sampling period.

[0034] Example: Calculate the ek and ec values ​​at each moment according to the formula;

[0035] Table 2: Schematic diagram of pressure deviation and deviation change rate S204, monitor the adjusted production line and extract the characteristic data of the production line within the set time period; Calculates the pressure fluctuation rate within a set time period .

[0036] in, It is the maximum value of real-time pressure data within the set time period; It is the minimum value of pressure data within the set time period; is the theoretical back pressure value.

[0037] Example: Extract feature data: time period t=0 to t=10; Pressure fluctuation rate: ; ; calculated P = 2.22; .

[0038] S205, through the formula Calculate the position-pressure coupling coefficient; compare the pressure fluctuation rate, response delay time, and position-pressure coupling coefficient analysis with the corresponding characteristic threshold; when the pressure fluctuation rate, response delay time, and position-pressure coupling coefficient are all less than the corresponding characteristic threshold, the test result is marked as passed; otherwise, the test result is marked as failed.

[0039] in, The change in real-time pressure within the set time period; It is the change of strip position within the set time.

[0040] Example: ; ; Then we can calculate it by the formula ;Response delay time is 3s; Compare the pressure fluctuation rate, response delay time and position-pressure coupling coefficient analysis with the corresponding characteristic thresholds; Pressure fluctuation rate ΔP=27.03%>5% (threshold), not satisfied; The response delay time is 3s > 2s (threshold), which is not met; The position-pressure coupling coefficient K=0.00144<0.1 (threshold value) is satisfied.

[0041] S206. When the test result is passed, maintain pressure control; otherwise, analyze the reasons for the different tests; when the pressure fluctuation rate is greater than the corresponding characteristic threshold, re-analyze the theoretical back pressure value and strengthen pressure control.

[0042] S207. When the response delay time is greater than the corresponding characteristic threshold, the communication connection is checked and reconnected; when the position-pressure coupling coefficient is greater than the corresponding characteristic threshold, the position of the strip is re-detected and marked.

[0043] For example, if the pressure fluctuation rate ΔP=27.03%>5%, the theoretical back pressure value is re-analyzed ( S202 ), and the pressure control is strengthened (for example, increasing the control gain or adjusting the filter parameters).

[0044] Response delay time 3s>2s: Check and reconnect the communication connection.

[0045] Based on the above steps, a backpressure analysis function was constructed that comprehensively considers multiple key factors, including the strip's material strength, thickness, width, production line speed, side guide opening, and the distance between the strip's position and the starting position. This provides a precise target value for subsequent pressure adjustments, helping to ensure the side guides apply just the right amount of pressure to the strip, reducing quality issues such as strip deformation and surface scratches caused by inappropriate pressure, and improving product qualification rates. A sliding mean filter was used to process the side guide cylinder backpressure values, smoothing the data and removing noise interference, making the acquired pressure values ​​more accurate and reliable. The pressure deviation and deviation change rate were then calculated and compared with a pressure database to determine adjustment parameters. The side guides were then adjusted, achieving dynamic and precise pressure control, further ensuring product quality. The adjusted production line was then monitored, extracting characteristic data within a set time period and calculating the pressure fluctuation rate. This provided real-time visibility into the stability of the production line's pressure, promptly detecting abnormal pressure fluctuations, and preventing the impact of unstable pressure on production, thereby ensuring the continuity and stability of the production process.

[0046] In a possible implementation of the embodiment of the present invention, combined with Figure 1 , Figure 3 Steps S104-S105 can be implemented by the following S301-S304, which are described in detail below: S301. Retrieve historical position data and corresponding pressure data of the steel strip; integrate the historical position data and pressure data into a historical analysis sequence.

[0047] S302 , integrating the real-time position of the steel strip and the historical analysis sequence into a pressure prediction sequence; calling a pressure prediction model, inputting the pressure prediction sequence into the pressure prediction model, and obtaining a corresponding predicted pressure.

[0048] The pressure prediction model is based on artificial intelligence model training, including: Select appropriate models and deep learning frameworks from the artificial intelligence model library; construct the model based on the deep learning framework to obtain a constructed model; Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the pressure prediction sequence and standard output data consistent with the content attributes of the predicted pressure; Divide the standard data set into training set, validation set and test set according to the set ratio; use the training set to train the constructed model; use the validation set to adjust the internal parameters of the constructed model; use the test set to test the constructed model and obtain the test indicators; When the test index is greater than the index threshold, the trained construction model is marked as a stress prediction model; otherwise, the stress test model is rebuilt and trained.

[0049] It should be noted that the test indicators include: accuracy, recall rate, stability and F1 score; the indicator thresholds are set based on actual experience.

[0050] S303, fitting historical pressure data of the strip into a pressure change curve; calculating a prediction threshold value based on the pressure change curve and a set maximum change rate; and comparing the predicted pressure with the prediction threshold value.

[0051] S304. When the predicted pressure is less than the predicted threshold, the valve opening is not adjusted; otherwise, the absolute value of the difference between the two is calculated to obtain the pressure difference; the pressure difference is matched with the corresponding mapping relationship table to obtain the adjustment amplitude, and the valve opening is adjusted according to the adjustment amplitude.

[0052] For example, the prediction model is used to predict the pressure, and the predicted pressure is: 12.2; the prediction threshold is calculated based on the pressure in the real-time data: 11.65; The calculated difference is 0.55; according to the corresponding mapping table, the valve opening is reduced by 10%.

[0053] Based on the above steps, the historical position data and corresponding pressure data of the strip are retrieved and integrated into a historical analysis sequence. The accumulated data resources provide rich and representative data samples for subsequent pressure prediction. The analysis of these historical data can help us understand the changing pattern of the pressure of the strip at different positions, thus laying a solid foundation for accurate pressure prediction; the historical pressure data of the strip is fitted into a pressure change curve, and the prediction threshold is calculated based on the curve and the set maximum change rate, which helps to avoid unnecessary adjustments due to small fluctuations in pressure and ensure the stability of the production process; decisions are made based on the comparison results of the predicted pressure and the prediction threshold; ensuring that the pressure on the strip is always within a reasonable range, improving product quality and production efficiency.

[0054] See also Figure 4, a second aspect of the present invention provides a control method system for optimizing a coiling side guide back pressure circuit in a hot rolling mill, comprising: a collection unit and a processing unit; The acquisition unit is used to collect real-time data of the side panels and production line through data sensors; The processing unit is used to dynamically adjust the pressure of the side plate according to real-time data; test the adjusted pressure and constrain the pressure according to the test results; predict the pressure according to the position data of the strip; and adjust the valve opening based on the prediction results.

[0055] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0056] The working principle of the present invention is as follows: the present invention collects real-time data of the side plate and the production line through data sensors; dynamically adjusts the pressure of the side plate according to the real-time data; tests the adjusted pressure and constrains the pressure according to the test results; predicts the pressure according to the position data of the strip; and adjusts the valve opening based on the prediction results.

[0057] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill, characterized in that: include: Collect real-time data of side panels and production lines through data sensors; Dynamically adjust the pressure of the side panels based on real-time data; Test the adjusted pressure; Detect and adjust anomalies based on test results; Predict the pressure based on the position data of the strip; The valve opening is adjusted based on the prediction results.

2. A control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 1, characterized in that: The real-time data is retrieved; wherein the real-time data includes: the back pressure value of the side guide cylinder, the side guide opening, the production line speed, the strip width and the strip position; The side plate back pressure value is analyzed according to the real-time data to obtain the theoretical back pressure value; the side guide plate is adjusted according to the compression of the side guide plate cylinder in the real-time data and the theoretical back pressure value.

3. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 2, characterized in that: The analysis of the side plate back pressure value according to the real-time data includes: Compare the side guide opening with the strip width and calculate the deviation between the two; integrate the production line speed within a set time period and mark the segments according to the acceleration of the production line; Construct a backpressure value analysis function: ; Wherein, k represents the process coefficient; is the material strength of the strip; H is the thickness of the strip; W is the width of the strip; V represents the production line speed; It is the maximum speed of the production line within the set time period; L represents the opening of the side guide plate; is the distance between the strip position and the starting position; The back pressure value of the side plate is calculated according to the back pressure value analysis function to obtain the theoretical back pressure value.

4. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 2, characterized in that: The adjusting of the side guide plate according to the side guide plate cylinder being pressed and the theoretical back pressure value in the real-time data includes: The back pressure value of the side guide plate cylinder is processed by using a sliding mean filter; the difference between the back pressure value of the side guide plate cylinder and the theoretical back pressure value is calculated to obtain the pressure deviation; By formula Calculate the deviation change rate of the back pressure value; where e(k) is the pressure deviation at the current moment; e(k-1) is the pressure deviation at the previous moment; and T is the sampling period. The absolute values ​​of the pressure deviation and the deviation change rate are compared with the pressure database to obtain corresponding adjustment parameters; the side guide plate is adjusted according to the adjustment parameters.

5. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 1, characterized in that: The adjusted production line is monitored to extract characteristic data of the production line within a set time period; wherein the characteristic data includes: real-time pressure data, response delay time and strip position; By formula Calculates the pressure fluctuation rate within a set time period ;in, It is the maximum value of real-time pressure data within the set time period; It is the minimum value of pressure data within the set time period; is the theoretical back pressure value; By formula Calculate the position-pressure coupling coefficient; where, The change in real-time pressure within the set time period; The change in the strip position within the set time; The pressure fluctuation rate, response delay time and position-pressure coupling coefficient analysis are compared with the corresponding characteristic thresholds; when the pressure fluctuation rate, response delay time and position-pressure coupling coefficient are all less than the corresponding characteristic thresholds, the test result is marked as passed; otherwise, the test result is marked as failed.

6. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 1, characterized in that: The detecting and adjusting of the abnormality according to the test results includes: If the test result is passed, maintain pressure control; otherwise, analyze the reasons for the different tests; if the pressure fluctuation rate is greater than the corresponding characteristic threshold, re-analyze the theoretical back pressure value and strengthen pressure control; When the response delay time is greater than the corresponding characteristic threshold, the communication connection is checked and reconnected; when the position-pressure coupling coefficient is greater than the corresponding characteristic threshold, the position of the strip is re-detected and marked.

7. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 1, characterized in that: The method of predicting the pressure according to the position data of the strip steel includes: Retrieve the historical position data and corresponding pressure data of the strip steel; integrate the historical position data and pressure data into a historical analysis sequence; The real-time position of the strip and the historical analysis sequence are integrated into a pressure prediction sequence; the pressure prediction model is called, and the pressure prediction sequence is input into the pressure prediction model to obtain the corresponding predicted pressure; wherein, the pressure prediction model is based on artificial intelligence model training.

8. A control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 7, characterized in that: The pressure prediction model is based on artificial intelligence model training, including: Select appropriate models and deep learning frameworks from the artificial intelligence model library; construct the model based on the deep learning framework to obtain a constructed model; Acquire a standard data set; wherein the standard data set includes standard input data consistent with the content attributes of the pressure prediction sequence and standard output data consistent with the content attributes of the predicted pressure; Divide the standard data set into training set, validation set and test set according to the set ratio; use the training set to train the constructed model; use the validation set to adjust the internal parameters of the constructed model; use the test set to test the constructed model and obtain the test indicators; When the test index is greater than the index threshold, the trained construction model is marked as a stress prediction model; otherwise, the stress test model is rebuilt and trained.

9. The control method for optimizing the back pressure circuit on the coiling side of a hot rolling mill according to claim 1, characterized in that: The adjusting of the valve opening based on the prediction result includes: Fitting historical pressure data of the strip into a pressure change curve; calculating a prediction threshold value based on the pressure change curve and a set maximum change rate; and comparing the predicted pressure with the prediction threshold value; When the predicted pressure is less than the predicted threshold, the valve opening is not adjusted; otherwise, the absolute value of the difference between the two is calculated to obtain the pressure difference; the pressure difference is matched with the corresponding mapping relationship table to obtain the adjustment range, and the valve opening is adjusted according to the adjustment range.

10. A control system for optimizing the coiling side guide back pressure circuit of a hot rolling mill, applied to the control method for optimizing the coiling side guide back pressure circuit of a hot rolling mill according to any one of claims 1 to 9, characterized in that: include: Acquisition unit and processing unit; The acquisition unit is used to collect real-time data of the side panels and production line through data sensors; A processing unit for dynamically adjusting the pressure of the side panels based on real-time data; The adjusted pressure is tested and the pressure is constrained according to the test results; the pressure is predicted according to the position data of the strip; and the valve opening is adjusted based on the prediction results.

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